B1034
Title: A Bayesian approach for estimating dynamic functional connectivity networks in fMRI data
Authors: Ryan Warnick - Rice University (United States)
Michele Guindani - University of California, Irvine (United States)
Erik Erhardt - University of New Mexico (United States) [presenting]
Elena Allen - Medici Technologies (United States)
Vince Calhoun - Mind Research Network (United States)
Marina Vannucci - Rice University (United States)
Abstract: Dynamic functional connectivity, i.e. how the interactions of brain regions change during an fMRI experiment, has recently received wide interest in the neuroimaging literature. We propose a principled Bayesian approach for determining time varying functional connectivity in a single subject fMRI experiment. Our method utilizes a hidden Markov model for classification of latent neurological states, achieving estimation of the connectivity networks in an integrated framework by borrowing strength over the entire time course of the experiment. Our modeling framework further assumes that the graph structures, which define the connectivity states at each time point, are related within a super-graph, so to allow immediate assessment of the differences between networks by computing posterior probabilities of both shared and differential edges in the time-varying functional networks. We assess the performance of our proposed approach in a simulation study, and also show results for an fMRI sensorimotor task experiment.
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